MULTISENSOR NONINVASIVE BLOOD GLUCOSE MONITORING SYSTEM
A Thesis by
Soumyashree Samadarsinee
Bachelor of Technology, Siksha O Anusandhan (SOA) University, 2011
Submitted to the Department of Electrical Engineering and Computer
Science and the faculty of the Graduate School of
Wichita State University
in partial fulfillment of
the requirements for the degree
of Master of Science
May 2015
© Copyright 2015 by Soumyashree Samadarsinee
All Rights Reserved
MULTISENSOR NONINVASIVE BLOOD GLUCOSE MONITORING SYSTEM
The following faculty members have examined the final copy of this thesis for form and
content, and recommend that it be accepted in partial fulfillment of the requirement for the
degree of Master of Science with a major in Computer Networking.
_______________________________________
Dr. Abu Asaduzzaman, Committee Chair
_______________________________________
Dr. Deepak Gupta, Committee Member
_______________________________________
Dr. Yi Song, Committee Member
iii
DEDICATION
To the Almighty, my family for their ultimate encouragement throughout my education and for
incomparable advice throughout my life and my gems Ishanee, Tisya, Adrisha, Swaraj,
Swayum and Aayana who always find a way to cheer me up.
iv
ACKNOWLEDGMENTS
I am very thankful to my thesis advisor Dr. Abu Asaduzzaman for his continuous
encouragement and support throughout my research work. His timely supervision of my work
and guidance allowed this research work to be completed on time. He always had time and
patience to guide me in accomplishing this work in spite of his busy schedule and offered me
assistance from time to time. It has been an honor to work for him as a graduate research
assistant. I express my sincere gratitude and thanks towards Dr. Deepak Gupta (Engineering
Technology Program) and Dr. Yi Song (EECS Department) for taking time from their busy
schedules and serve in my thesis committee.
I take pleasure in recognizing, the efforts of all those who encouraged and assisted me
both directly and indirectly with my experimental research. I specially want to thank to Gopinath
Jayakumar for teaching me COMSOL Multiphysics, Anvesh Kolluri, Abhishek Mummidi,
Balakumar Jayakumar, Priyanka Mohan, and Nithyanandhi Duraisamy for their support during
my research.
Finally, I acknowledge the WSU Computer Architecture and Parallel Programming
Laboratory (CAPPLab) facilities and research group for providing me with all the necessary
resources to conduct the research work, prepare the manuscript, and improve the quality of the
work and manuscript with special obligations to Kishore K. Chidella.
v
ABSTRACT
Diabetes and related complications are responsible for early death – one person in every 7
seconds. Long term and short term complications due to diabetes can be reduced through proper
diet, physical exercise, and medication. In order to assess the pattern of glucose changes of
diabetic patients (for determining the appropriate drugs), concentration of glucose in blood needs
to be monitored. The pain and inconvenience (due to pricking fingers) in the current invasive
blood glucose monitoring technique has led to the emergence of noninvasive blood glucose
monitoring (NIBGM) techniques. In this paper, we propose a multi-sensor NIBGM system using
infrared (IR) sensor and ultrasonic micro-electro-micro mechanical (MEMS) technology. We
simulate the proposed NIBGM system using COMSOL Multiphysics software and calibrate the
system using Matlab code. Lead free piezoelectric materials are evaluated for ultrasonic sensors.
Behavior of ultrasonic MEMS is simulated by varying the concentration of blood glucose. The
simulation results are cross validated with actual glucose concentration to assess errors.
According to simulation results and Clarke error grid analysis (EGA), the proposed NIBGM
system has potential to enhance accuracy. By adding easiness and comfort (due to no pricking),
the proposed multi-sensor NIBGM device should provide better assistance to manage diabetes.
vi
TABLE OF CONTENTS
Chapter
1.
Page
INTRODUCTION ................................................................................................................
1
The Diabetes Mellitus ...................................................................................................
1
1.1.1
Regulation of Blood Glucose Levels in Human Body ..........................................
1
1.1.2
Types of Diabetes ..................................................................................................
2
1.1.3
Complications related to Diabetes.........................................................................
3
1.2
Blood Glucose Level Monitoring .................................................................................
4
1.3
Micro-Electro-Mechanical Systems .............................................................................
6
1.4
Regression Analysis and Calibration ............................................................................
7
1.5
Problem Statement ........................................................................................................
7
1.6
Contributions ................................................................................................................
7
1.7
Thesis Organization ......................................................................................................
8
LITERATURE SURVEY .....................................................................................................
9
2.1 Invasive Blood Glucose Monitoring .........................................................................................
9
1.1
2.
2.2
Non Invasive Blood Glucose Monitoring ...................................................................
11
2.2.1 Properties of Skin and Glucose .................................................................................
12
2.2.1.1 Properties of Skin ............................................................................................
12
2.2.1.2 Properties of Glucose.......................................................................................
13
2.2.2 Optical Techniques for NIBGM ...............................................................................
14
2.2.2.1 Near Infrared (NIR) Spectroscopy .....................................................................
15
2.2.2.2 Mid Infrared (MIR) Spectroscopy......................................................................
17
2.2.2.3 Raman Spectroscopy ..........................................................................................
18
2.2.2.4 Occlusion Spectroscopy .....................................................................................
19
2.2.2.5 Optical Coherence Tomography ........................................................................
21
2.2.2.6 Fluorescence Technology ...................................................................................
22
2.2.2.7 Optical Polarimetry ............................................................................................
24
2.2.3 Photo Acoustic Spectroscopy for NIBGM ...............................................................
26
2.2.4
Electromagnetic Technique for NIBGM .............................................................
28
2.2.5
Bio-Impedance Spectroscopy for NIBGM ..........................................................
29
vii
TABLE OF CONTENTS (continued)
Chapter
2.3
2.4
2.5
2.6
3.
Page
Noninvasive Blood Glucose Monitoring Systems ......................................................
31
2.3.1 Benefits and Limitation of NIBGM Techniques .................................................
31
2.3.2 Devices Based on NIBGM ..................................................................................
33
Approach of Multisensor for NIBGM ........................................................................
34
2.4.1 Existing Sensors Based On Multi-Sensor Approach...........................................
34
2.4.2 Infrared and Ultrasound Technology – The Power of Two ................................
35
Micro-Electro-Mechanical System Technologies ......................................................
36
2.5.1 Introduction to MEMS ........................................................................................
36
2.5.2 Advantages of MEMS .........................................................................................
37
Regression Analysis Models for Calibration ..............................................................
37
2.6.1 Clarke Error Grid Analysis .................................................................................
38
2.6.2 Multiple Regression Analysis .............................................................................
39
PROPOSED NONINVASIVE BLOOD GLUCOSE LEVEL MONITORING SYSTEM 40
3.1
Overview ..................................................................................................................... 40
3.2
Proposed Method ........................................................................................................ 41
3.3
Block Diagram of the Proposed Method .................................................................... 41
3.4
Working Principle of the Proposed Method ............................................................... 43
4.
4.1
EXPERIMENTAL DETAILS............................................................................................. 46
COMSOL Multiphysics 4.3 ........................................................................................ 46
4.1.1
4.2
Matlab ......................................................................................................................... 47
4.2.1
5.
5.1
Boundary Conditions and Assumptions for Simulation ...................................... 47
Assumptions for Calibration System ................................................................... 47
RESULTS AND EVALUATION ....................................................................................... 49
Noninvasive Monitoring of Blood Glucose Level ...................................................... 49
5.1.1
Simulation of Different Piezoelectric Materials .................................................. 49
viii
TABLE OF CONTENTS (continued)
Chapter
5.2
5.3
Page
5.1.2Analysis of Data for Measuring Concentration of Glucose ................................
51
5.1.2.2 Using Ultrasonic MEMS Sensor .....................................................................
51
5.1.2.1 Using Infrared Sensor ......................................................................................
54
5.1.2.3 Using Proposed Technique ..............................................................................
55
Calibration Model for Measuring Blood Glucose Concentration ..............................
57
5.2.1Calibration Model in NIR Spectroscopy using IR Sensor ..................................
57
5.2.2Calibration Model in Proposed Technique..........................................................
59
Analysis of Error for the Prediction of Glucose Concentration .................................
60
5.3.1Analysis of Error using NIR Spectroscopy and Proposed Technique ................
60
5.3.2Clarke Error Grid Analysis for the Proposed Technique ....................................
62
5.3.3
Important Hardware Considerations ...................................................................
63
CONCLUSIONS AND FUTURE EXTENSIONS .............................................................
66
6.1
Conclusions ................................................................................................................
66
6.2
Future Extensions .......................................................................................................
67
REFERENCES .............................................................................................................................
68
6.
ix
LIST OF TABLES
Table
Page
1.
Glucose Absorption Band Spectral Characteristics ..........................................................12
2.
Benefits and Limitations of Various Noninvasive Glucose Monitoring Techniques ....... 29
3.
Various Noninvasive Glucose Monitoring Device ........................................................... 32
4.
Pressure Generated in Blood by Different Piezoelectric Materials …….……………….49
5.
Pressure Generated by BT in Blood for Various Glucose Concentration……………….52
6.
Absorption of IR for Various Glucose Concentration …….………………………….…54
7.
Absorption and Pressure Generated Value for Various Glucose Concentration …….….55
8.
Predicted Glucose Value for NIR Spectroscopy…………………...…………………….57
9.
Predicted Glucose Value for Proposed Technique……………...……………………….59
10.
Predicted Glucose Value for IR Spectroscopy and Proposed NIBGM System…….…....61
11.
Estimated overall cost of the device based on proposed technique……………………...65
x
LIST OF FIGURES
Figure
Page
1.
Regulation of Blood Glucose Levels by Insulin and Glucagon ………………..................2
2.
Symptoms of Diabetes.........................................................................................................3
3.
Invasive and Noninvasive Glucose Monitoring …..............................................................5
4.
Noninvasive Techniques for Glucose Monitoring………………………………………...6
5.
Regulation of Blood Glucose Levels in Invasive Way ………………….……………..…9
6.
Electrochemical Blood-Glucose Monitoring Strip ………………….………………..…11
7.
Skin Layered Structure and Distribution of Blood …………….......................................12
8.
Absorption Spectrum of Glucose ……………………………………..............................14
9.
Interaction of Light with Skin…………………………………..…………......................15
10.
Infrared Spectroscopy Principle…………… .................................................................... 16
11.
Glove Instrument …………………………………..........................................................17
12.
Cnoga Medical …………………………..........................................................................18
13.
Raman Spectroscopy …………………………………….................................................19
14.
C8 MediSensor using Raman Spectroscopy …..………...................................................20
15.
Occlusion Spectroscopy …………………………………………………………...…….20
16.
OrSense NBM-200G using Occlusion Spectroscopy. ………………..............................21
17.
Optical Coherence Tomography ……...............................................................................22
18.
Sentris-100 using Optical Tomography………………………………………………….23
19.
Diboronic Acid-fluorescence reagent used in Fluorescence Spectroscopy……………...23
20.
Fluorescence Spectroscopy Principle……………………………………………………24
xi
LIST OF FIGURES (continued)
Figure
Page
21.
Photo Induced Electron Transfer………………………………………………………...25
22.
Optical Polarimetry………………………………………………………………….…...25
23.
Experiment on Optical Polarimetry…………...………………………………………....26
24.
Photo Acoustic Spectroscopy………………………………………………………...….27
25.
Aprise Glucose Monitoring Device……………………………………………………...28
26.
Electromagnetic Coupling between Two Inductors………...……………………………28
27.
Glucoband using Electromagnetic Sensing……………………………………………...29
28.
Bio Impedance Spectroscopy………...…………………………………………………..29
29.
Biosensor Inc. using Bio impedance Spectroscopy…………………..………………….34
30.
GlucoTrack Based on Multisensor Approach……………………………………………35
31.
Clarke Error Grid Analysis………………………………………………………………40
32.
Block Diagram of Proposed NIBGM System…………...……………………………….42
33.
Geometry of Ultrasonic MEMS Sensor and Blood…..………………………………….47
34.
Waveform of Acoustic Pressure Generated in Blood….………………………………...51
35.
Predicted Glucose Value vs Actual Glucose Value for IR Spectroscopy and Proposed
NIBGM System…...................................................................................................................62
36.
Clarke Error Grid Analysis for the Proposed NIBGM System…………….……………64
xii
LIST OF ABBREVIATIONS
BNN
Barium Sodium Niobate
BT
Barium Titanate
CAPPLab
Computer Architecture and Parallel Programming Laboratory
LIN
Lithium Niobate
MIR
Mid Infrared
MEMS
Micro Electronic Mechanical System
NIBGM
Noninvasive Blood Glucose Monitoring
NIR
Near Infrared
WHO
World Health Organization
xiii
CHAPTER 1
INTRODUCTION
According to World Health Organization (WHO), the pervasiveness of diabetes was
estimated to be 9% among adults, 4.9 million deaths were caused by diabetes in 2014 and
diabetes will be the 7th leading disease cause of death in 2030. Diabetes and its complications
are responsible for early death, where 1 person dies in every 7 seconds [1]. With regards to
economics, cost of diabetes covers 6 to 15% of the budget of National Health System in the
European Union [2].
1.1
The Diabetes Mellitus
Diabetes Mellitus, commonly referred as diabetes, is a metabolic disease that occurs
when person has uncontrollable blood glucose levels over a long period.
1.1.1 Regulation of Blood Glucose Levels in Human Body
Glucose is the main source of energy for the human body. Glucose levels are regulated to
keep the body homeostasis so that blood glucose level remains stable and relatively constant.
There are many hormones that are involved in this process but insulin is the most important one.
Insulin is produced by the beta cells of the pancreas and it is provided to remove excess glucose
from the blood. It also acts a controlling signal to breakdown glucose to glycogen for internal
storage in blood [3]. As the blood glucose level increases, insulin stimulates the cell to utilize
more glucose and acts a controlling signal by sending the signal to the liver to convert excess
glucose to glycogen for the later use so that when there is a fall in blood glucose level, glucagon
helps the breakdown of glycogen into glucose as shown in Figure 1.
1
Figure 1. Regulation of Blood Glucose Levels by Insulin and Glucagon [3].
In this way, both insulin and glucagon work together to maintain the glucose level
constant and stable.
1.1.2 Types of Diabetes
The three most common type of diabetes are Type 1 diabetes, Type 2 diabetes, and
gestational diabetes. Although there are also other types of diabetes including congenital
diabetes, cystic-fibrosis related diabetes, and steroid diabetes [4].
Type 1 or Insulin Dependent Diabetes Mellitus (IDDM) is an auto immune disease in
which body cannot produce sufficient insulin leading to insulin deficiency due to loss of insulinproducing beta cells. Type 2 diabetes or Non-Insulin Dependent Diabetes Mellitus (NIDDM) is
due to reduced insulin secretion. Gestational diabetes usually occurs to women during their
pregnancy. It generally resolves after pregnancy or else it may lead to Type 2 diabetes. 90%
cases of diabetes are of Type 2 diabetes which mostly affects the adult people.
2
1.1.3 Complications related to Diabetes
Blood glucose level is the amount of glucose present in blood .It is commonly measured
in mg/dl. Diabetes patients have abnormally excessive glucose level or diminished glucose level.
Symptoms of diabetes include Polydipsia, Polyphagia, etc. as shown in Figure 2. This condition
can be classified as hypoglycemic, where blood glucose level is less than 72mg/dl or
hyperglycemic, where blood glucose level is more than 200mg/dl [5].
Figure 2. Symptoms of Diabetes [6]
Hyperglycemia has no immediate damaging effects for patients but it has long-term
complications. The long term complications include nerves damage, renal failure, blindness,
coronary heart disease, strokes and peripheral vascular disease. In order to prevent the above
complications, patients should have a proper dietary management, physical activity and use of
proper medications like insulin injections or tablets before meals.
Hypoglycemia has short term effects and it usually affects brain. It is classified on the
basis of glucose level:
 Mild hypoglycemia: Blood glucose level is between 55 and 70 mg/dl. It is characterized by
palpitations, extreme hunger, trembling, cold or excessive sweating and visual paleness due 
3
to poor peripheral blood circulation. In this case, eating small amount of carbohydrate can
restore normal glucose levels.
 Moderate hypoglycemia: Blood glucose level is between 40and 55 mg/dl. It is characterized
by mood changes, confusion, blurred vision, weakness and drowsiness since it affects the
central nervous system. 

 Severe hypoglycemia: Blood glucose level is less than 40 mg/dl. It is characterized by
convulsions, loss of consciousness and coma. In this case glucagon injection is required [7]. 
A patient needs to monitor the blood glucose level on daily basis to control blood sugar levels.
1.2
Blood Glucose Level Monitoring
Long term and short term complications can be reduced through proper diet, physical
exercise, and medication. But to know the pattern of glucose changes of a diabetic patient,
concentration of glucose in blood (glycaemia) needs to be monitored. Blood glucose level can be
monitored in minimal invasive way and noninvasive manner. Currently, glucose monitoring
techniques that are minimal invasive are in practice.
Minimal invasive technique is done by piercing the skin, typically the finger tip, to draw
blood and drop blood onto a reagent test strip and determine the glucose concentration by
inserting the strip into the measurement device. This technique converts the glucose
concentration into electrical signal. Mostly it works on glucose-oxidase principle. Other
principles based on binding of glucose with other molecules or glucose spectral properties [8].
4
Figure 3. Invasive (Left) and Noninvasive (Right) Glucose Monitoring [9, 10]
Noninvasive technique measures glucose concentration through skin without extracting
blood or interstitial fluid or without a needle penetrating through skin for reaching these fluids. It
measures the physical properties of the fluid or underlying tissue like optical, acoustic, and
electrical properties whose value changes with any change in blood glucose level. Various
noninvasive technique in research are MIR/NIR Spectroscopy, Raman Spectroscopy, Occlusion
Spectroscopy, Optical Coherence Tomography, Fluorescence, Polarimetry, Photo acoustic
Spectroscopy, Impedance/Dielectric Spectroscopy, Electromagnetic as illustrated in Figure 4.
These techniques are affected by external factors, making it more difficult to perform an accurate
measurement [10].
5
Figure 4. Noninvasive Techniques for Blood Glucose Monitoring
1.3
Micro-Electro-Mechanical Systems
Micro-Electro-Mechanical System (MEMS) is the method to combine electrical and
mechanical components together on a chip, to make a system of miniature scale.
In the United States they are mainly called MEMS, whereas in some other parts of the
world they are called “Microsystems Technology” or “micro machined devices”.
Now-a-days, MEMS are being preferred for sensors because of the following reasons:







Size is smaller as compared to actual sensor. 
Power Consumption is low. 
Sensitivity to input variation is high. 
Cost is less. 
6
1.4
Regression Analysis and Calibration
Noninvasive method of glucose monitoring is partial in the prediction of blood glucose.
There are two reasons responsible for this inaccuracy. It includes problem related to engineering
and second, may be due to data analysis problem. Partial work of our proposed technique is to
find out the best regression model and implementation it as calibration model. Calibration is
essential to achieve the uniformity in the measurement. Every so often calibration comprises
forming the relationship between a device response (response of sensors in this case) and one or
more reference values (actual glucose value in this case).
1.5
Problem Statement
Invasive method of blood glucose monitor has many shortcomings such as pain,
discomfort, and risk of infection. Therefore, cost-effective noninvasive glucose monitor is
desirable. However, contemporary noninvasive glucose monitors are not capable to give the
required accuracy because of engineering problems and statistical issues to analyze the data.
1.6
Contributions
In this work, an ultrasonic MEMS sensor and near infrared sensor transceivers assembly
are introduced for blood glucose predictions by noninvasive approach. The goal of this project is
to improve the accuracy of noninvasive technique to predict glucose level. The major
contributions of this work include:





Introduction of ultrasonic MEMS sensor along with near infrared sensor to overcome the
disadvantage of near infrared noninvasive technique and photo acoustic spectroscopy. 
Improvement of the accuracy and sensitivity of the device. 
Implementation of the best regression model for calibrating the device. 
7
1.7
Thesis Organization
The rest of the manuscript is organized as follow:
In Chapter 2, we discuss invasive and noninvasive techniques of glucose
monitoring system and about regression analysis method and calibration that are explained
in various conference and journal papers.
In Chapter 3, we introduce the proposed multisensor noninvasive blood glucose
monitoring system.
In Chapter 4, we describe the system parameters, experimental setup and input
parameters that are used for this experiment.
In Chapter 5, we present experimental results to evaluate the proposed technique.
In Chapter 6, we conclude this work and list of possible future extensions of this
proposed technique.
8
CHAPTER 2
LITERATURE SURVEY
In this chapter, some related published articles are discussed to understand various
invasive and noninvasive techniques of glucose monitoring and various regression
analysis methods.
2.1 Invasive Blood Glucose Monitoring
In invasive glucose meter, patients monitor their blood glucose levels by keep small
amount of their blood on test strip of glucose monitor device as shown in Figure 5.
Figure 5. Regulation of Blood Glucose Levels in Invasive Way [11]
Invasive glucose monitor device based on the principle of electrochemical cell and it
contains two major parts. They are:
 Testing strips: These are single use. It is a small piece of paper which has certain chemicals,
which allow it to react with glucose in the blood. It has a hard plastic base to give strength to 
the strip as shown in Figure 6. Several chemical layers are separated by spacers. One layer
9
contains enzyme glucose oxidase, another contains potassium ferricyanide. Another layer has
two electrodes to measure current flow. In between these layers ,one layer of chemical
present which protect the above layers and helps to react all layers with blood.
 Monitor: Monitor measures the electrical current flow through the test strip and change the
current conferring to the amount of glucose present in blood. 
Figure 6. Electrochemical Blood-Glucose Monitoring Strip [12]
When patient puts small amount of blood on test strip, the blood is pulled up the sides of
the test strip through capillary action. As the blood flows into the test strips, it comes in contact
with glucose oxidase layer. Then Glucose oxidase enzyme (GOD) reacts with glucose in blood
and produces gluconic acid.
glucose + GOD(ox) → gluconolactone + GOD (red)
glucose → gluconolactone + 2e2e- + GOD(ox) → GOD (red)
10
Then the gluconic acid reacts to potassium ferricyanide layer to form potassium
ferrocyanide. Then potassium ferrocyanide reacts with metals in the electrode and cause current
to flow in electrode layer.
So the more glucose in blood, more gluconic acid will form. More gluconic acid will
form, more ferrocyanide will form. And more ferrocyanide, more current will flow through
electrodes [12].
Eventhough invasive type glucose monitoring provides accurate results and active
management of glucose level, it has many disadvantages too, like:

Cost of blood glucose test strips, tends to increase the recurring cost of diabetic device. As
test strips are single use only. 


Discomfort for the patient as some patients are not comfortable with sharp device or seeing
their blood. 


Inconvenience for patient for frequent testing as with every time testing, they are losing some
amount of blood from their body. 


Patients need to carry required equipment and supplies. Starting from cotton, alcohol to
glucose monitor, they need to carry for not getting infection later. 
2.2
Non Invasive Blood Glucose Monitoring
Noninvasive Blood Glucose Monitoring are in research area for obvious reason related to
comfort of patient. Even if they do not provide accurate results, there has been gradually a
increase in the development of these noninvasive technologies. Before going to individual
technology, the properties of skin and glucose are discussed which will be the base for measuring
blood glucose level. Then the different noninvasive technologies are discussed with their
respective working principles, advantages and disadvantages.
11
2.2.1 Properties of Skin and Glucose
To design a noninvasive glucose monitoring device, it is necessary to know about the
properties of skin and glucose. Due to the noninvasive approach, properties of skin plays a vital
role to determine the accuracy of the device.
2.2.1.1 Properties of Skin
To understand the characteristics of noninvasive glucose monitoring sensors, it is
convenient to know the skin morphology and distribution of blood within the layers.
Skin is composed of several layers as exemplified in Figure 7. The uppermost layer is
stratum corneum of epidermis, composed of dead keratinized. Second layer is epidermis and
followed by dermis. Below to that is the subcutaneous tissue, which is composed of underlying
fat and muscle. The dermis is again subdivided into three different layers: upper vascular plexus,
reticular dermis, and deep vascular plexus. Epidermis does not contain its own vascular system.
Fraction of blood vessels in the dermis is in the range of 1%-20%, mainly in upper and deep
vascular plexus [13].
Figure 7 Skin Layered Structure and Distribution of Blood [13]
12
Most noninvasive glucose sensors exploit different properties to interact with skin which
can measure blood glucose level. These properties include optical, acoustic, and electric.
2.2.1.2 Properties of Glucose
Blood has average density of 1060 kg/mm3 with average glucose level of 70 mg/dl. But
there will be a fluctuation of blood glucose level during the course of a day [14]. So density of
blood will increase with increase in glucose level.
Both physical and chemical properties of glucose should be studied in order to design a
noninvasive glucose sensor. Table 1 [15] shows the spectral characteristics for absorption band
of glucose and Figure 8 shows the absorbance spectrum of glucose [16]. Absorption coefficient
of glucose is also shown in Figure 9 with feasible region for the measurement of glucose [17].
Table 1. Glucose absorption band spectral characteristics
Wavelength (nm)
Possible Assign.
Description
939
3 v O-H stretch
A second O-H overtone band
1126
3 v C-H stretch
A second harmonic C-H
overtone band
1408
2v O-H
A first O-H overtone band
1538
v O-H + v C-H
O-H and C-H combination
band
1688
2v C-H
A C-H overtone band
2261
v C-H + v C-C-H + O-C-H
Combination of a CH stretch
and a CCH, OCH deformation
2326
2v O-H
A first O-H overtone band
13
Figure 8. Absorption spectrum of glucose [16, 17]
It is observed from Figure 8 that the absorption of glucose which is measured as
absorbance (unit A.U- Absorbance Unit which is unit less) has the highest peak at wavelength
1440 nm and 2270 nm. But at 1440 nm, absorption of infrared due to water is also high. So it
will be difficult to distinguish the characteristics of glucose at 1440 nm. So wavelength of 2270
is preferred.
It is also observed that the absorption coefficient depends on wavelength for a specific
material. Here the value of absorption coefficient is high in the range of 2000 to 3000 nm. Again
feasible range to measure the blood glucose level lies in the green region. As the cost of the
device is also a major concern, semiconductor diodes are of less cost when compared to
Quantum Cascade Lasers. Due to which 1440 nm to nearly 4000 nm would be feasible for the
measurement.
2.2.2 Optical Techniques for NIBGM
When light passes through skin, it undergoes various properties of light like Reflection,
Scattering and Absorption. It is reflected by the stratum corneum, absorbed by the skin and
14
remaining part is scattered and diffused into multiple directions. Figure 9 shows the interaction
of light with skin.
Figure 9. Interaction of Light with Skin [15]
These optical properties of light are useful for detecting glucose level in blood.
Spectroscopy analyses the optical properties of light with various wavelength of radiation.
Spectroscopy is also helpful for finding the constituent in the material along with their
concentration since each constituent exhibit different spectral properties. Different sensors based
on the spectroscopy principle can be classified according to the optical properties of light which
is applied.
2.2.2.1 Near Infrared (NIR) Spectroscopy
Near infrared spectroscopy is based on Absorption-Transmittance Photometry as shown
in Figure 10. Changes in glucose concentration in blood level can change in the amount of light
absorbed by the skin.
15
Figure 10. Infrared Spectroscopy Principle [16]
In near infrared spectroscopy, light in near infrared range (750-2000 nm) is used. Light of
these wavelengths pass through the stratum corneum and measures the light absorbed in the deep
tissue in the range of 1mm to 100 mm of depth due to the glucose in blood [16].
Benefits











The photoconductive detectors which is used for NIR Spectroscopy is highly sensitive. 
Water absorbs less near infrared. So it’s very useful for measuring blood glucose level. 
Strength of signal is high as compared to mid infrared spectroscopy. 
Materials which are used for infrared spectroscopy are low in cost and available in wide
range. 
Tested for human successfully. 
Safe for human body. 
Restrictions





Multivariate analysis is required. 
Device needs to be in miniaturization scale. 
Factors like blood pressure, body temperature and skin hydration are tormenting effects the
blood glucose measurement. 




External factors like temperature, humidity, carbon dioxide, and atmospheric pressure can
also cause error. 
Hardware is less sensitive and stable for near infrared sensor [17]. 
16
NIR Spectroscopy Based Glucose Monitoring Device
Glove instrument and Cnoga medical developed glucose monitoring device based on near
infrared principle as shown in Figure 11 and Figure 12 [18, 19].
Figure 11. Glove Instrument [18]
Figure12. Cnoga Medical [19]
2.2.2.2 Mid Infrared (MIR) Spectroscopy
In mid infrared spectroscopy, light in mid infrared region (2500-10000 nm) is used. It
employs same principle as near infrared spectroscopy. With change in concentration of glucose
in blood, there is change in absorption of mid infrared.
Benefits



Scattering effect is less and still increased absorption compare to NIR spectroscopy due to
higher wavelength. 
Response peak of biological compounds are sharper with MIR than NIR [20]. 
Limitations



Mid infrared penetration through skin is very poor. 
Absorption of mid infrared due to water is more compared to glucose in blood [21]. 
17
2.2.2.3 Raman Spectroscopy
Raman Spectroscopy is based on the principle of scattering effect of light. It uses laser
light in the range of 200 cm-1 to 2,000 cm-1 to prompt oscillation in glucose contained fluid.
Intensity of scattered light depends on the rotational and vibrational energy of glucose molecule,
which is base for measuring the glucose concentration in human body [22]. This is illustrated in
Figure 13 [23].
Figure 13. Raman Spectroscopy [23]
Benefits





It provides sharper spectra compared to NIR and MIR spectroscopy. 
It is less sensitive towards temperature. 
It is very less sensitive to water. 
Limitations





Laser wavelength and intensity is very unstable. 
Light source is very harmful for human body. 
Interference from other biological compounds like hemoglobin is high. 
18



Collection time is long. 
Not tested in human. 
Raman Spectroscopy Based Glucose Monitoring Device
C8 MediSensor is developed which is based on the principle of Raman Spectroscopy
which is shown in Figure 14 [24].
Figure 14. C8 MediSensor using Raman Spectroscopy [24]
2.2.2.4 Occlusion Spectroscopy
Occlusion Spectroscopy is based on the scattering phenomenon of light. With increase in
level of glucose, results into decreasing the diffusion coefficient of light and boosted the
transmission of light as shown in Figure 15. This technique will happen after the erythrocyte (a
red blood cell in humans that is typically a biconcave disc without a nucleus) aggregation which
is done by applying pressure on the skin. First signal is collected without applying any pressure
and it is combined with the occlusion signal (after applying pressure) in order to calculate
glucose concentration [25].
19
Figure 15. Occlusion Spectroscopy [25]
Benefits

It measures glucose level of blood in artery. 
Limitations
 Previous erythrocyte aggregation and fat deposition greatly interfere with the measurement of
glucose.
Occlusion Spectroscopy Based Glucose Monitoring Device
OrSense NBM-200G is a glucose monitoring device which is based on the occlusion
spectroscopy [26] is shown in Figure 16.
Figure 16. OrSense NBM-200G using Occlusion Spectroscopy [26]
20
2.2.2.5 Optical Coherence Tomography
Optical Coherence Tomography was originally developed for tomographic imaging of the
eye. It uses a laser source with low power, an interferometer and a photodetector to measure the
interferometric signal as shown in Figure 17.
Figure 17. Optical Coherence Tomography [27]
It uses low coherence light where the emitted photons are synchronized in time and
space. The skin is irradiated with a low coherence light. Radiations then scattered from the tissue
and combine with the light returned from reference arm and results into interferometric signal
which is then detected by photodetector.
With increase in concentration of glucose in the interstitial fluid, the refractive index of
glucose also increases. This will create a mismatch between reference and sample indices and
hence the concentration of glucose in human body can also be measured [27].
Benefits

High signal to noise ratio as interferometric signal can be only formed within coherence
source. 
21

High resolution and penetration is also high due to the coherence sources. 
Limitations



Effect of skin temperature is very high on the measurement for higher degrees and leads to
inaccuracy. 
It is very sensitive to motion of individuals [28]. 
Optical Coherence Tomography Based Glucose Monitoring Device
Sentris-100 glucose monitoring device is based on optical coherence tomography
technology as illustrated in Figure 18.
Figure 18. Sentris-100 using Optical Tomography [29]
2.2.2.6 Fluorescence Technology
Fluorescence Technology is based on the principle that human tissue will generate
fluorescence when excited by lights at specific frequencies.
This technique uses fluorescence reagents which are shown in Figure 19 which helps to
track the presence of glucose molecules in blood.
22
Figure 19. Diboronic Acid-fluorescence reagent used in Fluorescence Spectroscopy [30]
Based on the affinity principle, fluorescence resonance energy transfers between a
fluorescent donor and an acceptor as seen in Figure 20. With increase in glucose level, glucose
molecule will also increase leading to generation of more fluorescence energy [31].
Figure 20. Fluorescence Spectroscopy Principle [31]
Benefits

This technology is highly sensitive. It can detect single glucose molecule. 
23
Limitations



It is highly affected by scattering phenomenon of light. 
Life span is short and it is not bio compatible. 
Fluorescence Technology based Glucose Monitoring Device
SCOUT DS is the device based on fluorescence technology as seen in Figure 21. It was
invented in 2011 and has received approval from health Canada for commercial distribution [32].
Figure 21. Photo Induced Electron Transfer [32]
2.2.2.7 Optical Polarimetry
Chiral molecules are the ones which are asymmetric in such a way that the structure and
its mirror image are not superimposable. Typically, chiral compounds are optically active.
Glucose is an example of chiral compounds which is base of the working of optical polarimetry.
When polarized light, all waves oscillating in the same plane, passes through the solution
containing chiral molecules, its polarization plane is rotated by a certain angle, which depends on
concentration of chiral molecule and chiral molecule is glucose in this case as seen in Figure 22.
Based on this, the glucose level can be measured by passing the polarized light into
aqueous humor like tear. For this reason, the preferred site is the eye [33, 34].
24
Figure 22. Optical Polarimetry [33]
Benefits



Light absorption and scattering effects in eye is low as there are no large proteins in aqueous
humor and main component is glucose. 
Visible light is used in this technique and parts can be easily scaled down. 
Limitations





Skin cannot be used for monitoring glucose level due to scattering effect. 
Eye movement is the major source of error. 
Presence of albumin and cholesterol makes the specificity of this technique poor. 
Optical Polarimetry Based Glucose Monitoring Device
Efficient device based on this technology has not been developed yet. Researchers have
done the experiments on rabbit’s eyes using dual wavelength as shown in Figure 23 [35].
25
Figure 23. Experiment on Optical Polarimetry [35]
2.2.3 Photo Acoustic Spectroscopy for NIBGM
Photo acoustic spectroscopy for NIBGM is based on the principle that when an energy
source exposes the skin, it causes thermal expansion. Due to the thermal expansion, acoustic
waves are released. Usually acoustic wave is measured in terms of pressure. In other words,
when the skin gets irradiated from energy source, it generate pressure as a wave which depends
on the density.
With increase in glucose concentration in body, it increases the density of the medium
(blood). With increase in density, pressure generated from energy source will also change, hence
the glucose level in human body can be measured [36]. Overall mechanism is illustrated in below
Figure 24.
26
Figure 24. Photo Acoustic Spectroscopy
Benefits

Sensitivity is higher than other spectroscopy in determination of glucose as photo acoustic
response of blood is better than water. 
Limitations



Sensitive to fluctuation of temperature. 
Instrument is expensive. 
Photo Acoustic Based Glucose Monitoring Device
Device named Aprise is developed which is based on the principle of photo acoustic
spectroscopy principle as shown in Figure 25.
Figure 25. Aprise Glucose Monitoring Device [37]
27
2.2.4 Electromagnetic Technique for NIBGM
This technique accesses the dielectric properties of blood using electromagnetic coupling
between inductors wrapped around the medium. Voltage at particular frequency is applied to one
of the two inductors and signal due to electromagnetic coupling is produced in other inductor.
Mechanism is shown in below Figure 26.
Figure 26. Electromagnetic Coupling between Two Inductors [38].
With change in glucose concentration, dielectric parameters will also change. This leads
to change in electromagnetic coupling signals. Hence the value of glucose level can be measured
[39].
Benefits



It minimizes the effect of cholesterol. 
Safe for human body. 
Limitations



Effect of temperature on measurement is very high. 
Dielectric properties of blood depend on other components of blood, not just glucose. This 
leads to inaccuracy for the measurement.
28
Electromagnetic Technique Based Glucose Monitoring Device
Glucoband is the glucose measuring device based on electromagnetic coupling principle.
It is shown in Figure 27 [40].
Figure 27. Glucoband using Electromagnetic Sensing [40]
2.2.5 Bio-Impedance Spectroscopy for NIBGM
Impedance spectroscopy is based on the method of measuring the resistance of the
medium after applying electric current. Spectrum in impedance spectroscopy is measured in
range of 0.1 to 100 MHz frequency [41].This is shown in Figure 28.
Figure 28. Bio- Impedance Spectroscopy [42]
29
With change in glucose level in blood, ion concentration of blood will also change. This
will result in change of impedance of blood.
Benefits





It doesn’t need any statically derived calibration model. 
Easy to use 
Low cost 
Limitations



Effect of water content in body is high. 
It needs long time for equilibrium process. 
Bio-Impedance Spectroscopy Based Glucose Monitoring Device
Biosensor Inc. developed a device based on the above principle and is under
development. It is seen in Figure 29.
Figure 29. Biosensor Inc. using Bio - Impedance Spectroscopy [43]
30
2.3
Noninvasive Blood Glucose Monitoring Systems
Different types of NIBGM techniques are discussed with their advantages and
disadvantages. In this section we will summarize the benefits and limitations of all NIBGM
techniques and developed devices based on this technologies.
2.3.1 Benefits and Limitation of NIBGM Techniques
The advantages and disadvantages of NIBGM techniques are summarized and shown in
Table 2.
Table 2. Benefits and Limitations of Various Noninvasive Glucose Monitoring Techniques
Noninvasive
Glucose
Monitoring
techniques
Benefits




Near
Infrared
(NIR)
Spectroscopy



Mid
Infrared
(MIR)
Spectroscopy

Limitations
Tested for human successfully
Not harmful for human body
Effect of interference is less
The photoconductive detectors
which is used for NIR
Spectroscopy
is
highly
sensitive.
Water absorbs less
near
infrared. So it’s very useful
for measuring blood glucose
level.
Strength of signal is high as
compared to mid infrared
spectroscopy.
Scattering effect is less and
still
increased
absorption
compare to NIR spectroscopy
due to higher wavelength.
Response peak of biological
compounds are sharper with
MIR than NIR
31







Multivariate analysis is required
Device needs to be miniaturization
scale
Scattering effect is more
Factors like blood pressure, body
temperature and skin hydration are
tormenting effects the blood glucose
measurement.
External factors like temperature,
humidity, carbon dioxide,
and
atmospheric pressure can also cause
error.
Mid infrared penetration through skin
is very poor.
Absorption of mid infrared due to
water is more compared to glucose in
blood
Table 2. (continued)


Raman
Spectroscopy
Occlusion
Spectroscopy



Optical
Coherence
Tomography


Fluorescence
Technology

Optical
Polarimetry

Photo acoustic
Spectroscopy
Electromagnetic
Sensing




Bio-Impedance
Spectroscopy


It provides sharper spectra
compared to NIR and MIR
spectroscopy.
It is less sensitive towards
temperature.
It is very less sensitive to
water.
It measures glucose level of
blood in artery.
High signal to noise ratio as
interferometric signal can be
only formed within coherence
source.
High
resolution
and
penetration is also high due to
the coherence sources.
This technology is highly
sensitive. It
can detect single
glucose molecule.
Light
absorption
and
scattering effects in eye is low
as there are no large proteins
in aqueous humor and main
component is glucose.
Visible light is used in this
technique and parts can be
easily scaled down.
Sensitivity is higher.
Safe for human body.
It minimizes the effect of
cholesterol.
Instrument is low in cost
Effect of cholesterol is less.
It doesn’t need any statically
derived calibration model.
32



















Laser wavelength and intensity is
very unstable.
Light source is very harmful for
human body.
Interference from other biological
compounds like hemoglobin is high.
Collection time is long
Not tested in human
Previous erythrocyte aggregation and
fat deposition greatly interfere with
the measurement of glucose.
Effect of skin temperature is very
high on the measurement for higher
degrees and leads to inaccuracy.
It is very sensitive to motion of
individuals
It is highly affected by scattering
phenomenon of light
Life span is short and it is not bio
compatible.
Skin cannot be used for monitoring
glucose level due to scattering effect.
Eye movement is the major source of
error.
Presence of albumin and cholesterol
makes the specificity of this
technique poor.
Sensitive
to
fluctuation
of
temperature.
Instrument is expensive.
Effect of temperature is high
Dielectric properties of blood depend
on other components of blood, not
just glucose. This leads to inaccuracy
for the measurement.
Calibration process is long
Effect of sweat, movement and
temperature is high.
2.3.2 Devices Based on NIBGM
With the emerging field in research for NIBGM techniques, there are some devices based on
various techniques. Devices along with technology and status are shown Table.3
Table 3. Various Noninvasive Glucose Monitoring Device
Device/company
Technology
Cnoga Medical
Near Infrared
Spectroscopy
(NIR) Developed in 2010 http://www.cnoga.co
and delivered to FDA m/Medical/Products/
for approval in 2011
Glucometer.aspx
Glove Instruments
Near Infrared
Spectroscopy
(NIR) Developed in 2008
C8 MediSensors
Raman Spectroscopy
Sentris-100
SCOUT
VeraLight Inc
Aprise
Status
URL
Developed in 2011 http://www.c8medise
and is investigational nsors.com/us/home.ht
status
ml
Optical
Coherence Appeared in 2009 in http://cloudTomography
computing.tmcnet.co
Europe market
m/news/2007/08/29/2
895935.htm
DS, Fluorescence
Appeared in 2011 and http://www.veralight.
Technology
has received approval com/products.html
from health Canada
for
commercial
distribution
Photo
acoustic Developed in 2005
Spectroscopy
Glucoband
http://groveinstrument
s.com/
Electromagnetic
Sensing
Developed
in
and is under
construction
33
http://professional.dia
betes.org/Abstracts_D
isplay.aspx?TYP=1&
CID=47288
2005 http://www.calistome
dical.com/
pilot
2.4
Approach of Multisensor for NIBGM
In comparison to invasive glucose monitoring, noninvasive way of glucose monitoring
always faces problem of inaccuracy. Various factors are responsible for these limitations of
noninvasive glucose monitoring. To find a solution for this problem, multi-sensor approach is
into consideration in last years. It consists of the combination of some of the above-mentioned
noninvasive techniques of glucose monitoring for better performance in terms of sensitivity and
specificity. There are some properties which may affect the measurement of glucose level, such
as:



Environmental factors like temperature or humidity. 
Effect of water or other biological compound. 
According to earlier discussions it is evident that each technique has its own advantage
and disadvantages. So combining into multi-sensor approach one might deal with above
problem. Apart from above problem, one important factor which is responsible for accuracy is
the calibration model using different regression analysis.
2.4.1 Existing Sensors Based on Multi-Sensor Approach
GlucoTrack is the noninvasive glucose monitoring device based on the Multisensor
approach. It is developed by Integrity Applications. GlucoTrack is based on three technologies
namely Photo acoustic spectroscopy, electromagnetic sensing and thermal emission technology.
It is shown in Figure 30.
34
Figure 30. GlucoTrack Based on Multisensor Approach [45].
Clip like parts need to be hold the ear. Each of the technologies provide a signal that is
calibrated into a glucose level with a suitable regression analysis model. This device price is
estimated to be $2000 with personal ear clip of $100 which need to be replaced every six
months. Cost factor is the disadvantage in this case [45].
Amaral and Coworkers Company developed a prototype which is based on near infrared
and mid infrared spectroscopy [46].
Swiss medical company, Solianis Monitoring AG also developed a device based on near
infrared spectroscopy along with several other sensor embedded in same substrate to know the
effect of temperature, pressure and humidity [47].
2.4.2 Infrared and Ultrasound Technology – The Power of Two
Infrared is a type of electromagnetic radiation of wavelength between roughly 700 nm to
300 µm which is longer than the wavelength of visible light. It is emitted by all living beings and
surrounding objects. It is frequently helpful to think of infrared in terms of radiated heat in terms
of transmission and absorption.
35
Ultrasound is an oscillating pressure wave of sound of frequency greater than hearing
capacity of human. Piezoelectric transducer is used for getting ultrasound which converts
electrical energy into an ultrasonic wave.
Infrared Sensor and ultrasonic sensor are combined together for maximum accuracy. This
technology will overcome the lack of sensitivity of infrared sensors and increase the specificity
of both infrared and ultrasonic sensors [48, 49].
Factors which were already discussed as disadvantages for near infrared technology and photo
acoustic technology are also to be considered. They are:



Near infrared spectroscopy based glucose monitoring device needs to be scaled in
miniaturization scale. 
Ultrasonic sensors used in photo acoustic are very costly. 
To overcome the above disadvantages, it is preferred to accept the MEMS technology.
2.5
Micro-Electro-Mechanical System Technologies
Infrared Sensor and ultrasonic sensor are combined together for maximum accuracy. This
technology will overcome the lack of sensitivity of infrared and increase the specificity of both
infrared and ultrasonic sensors. Size of the system also becomes a shortcoming factor for any
technology including NIBGM which not only affect the cost but also the portability. To
overcome this it’s needed to be scaled down in miniature form. Henceforth we concentrate on
MEMS technology which exhibit substantial meaning for miniaturized system.
2.5.1 Introduction to MEMS
Micro-Electro-Mechanical System (MEMS) is the method to combine electrical and
mechanical components together on a chip, to make a system of miniature scale.
There are type of sensors which can be used as MEMS. They are
36







Mechanical Sensors 
Optical Sensors 
Thermal Sensors 
Chemical and Biological Sensors 
2.5.2 Advantages of MEMS
Now-a-days MEMS are being preferred for sensors because of the following reasons:











2.6
Size is smaller as compared to actual sensor. 
Flexible design. 
Power Consumption is low. 
Sensitivity to input variation is high. 
Collaboration phenomenon with different frequencies for integrating with other supportive
system. 
Cost is less. 
Regression Analysis Models for Calibration
Noninvasive method of glucose monitoring is partial in the prediction of blood glucose.
There are two reasons responsible for this inaccuracy. It includes problem related to engineering
and second, may be due to data analysis problem. Partial work of our purpose technique is to
find out the best regression model and implementing it as a calibration model. Calibration is
essential to achieve the uniformity in the measurement. Every so often calibration comprises
forming the relationship between a device response (response of sensors in this case) and one or
more reference values (actual glucose value in this case). Linear regression is one of the most
commonly used statistical methods in calibration. Once the relationship between the reference
37
value and the response value is established, the calibration model is used in converse to predict a
value from a device response.
2.6.1 Clarke Error Grid Analysis
There are two factors which are responsible for the model’s performance. They are Average
prediction error in percentage (PAPE) and percent of acceptable points by “± 20% rule” as
shown in equation 1.
Average prediction error (APE) = 1
n
| − ′| …………………………… (1)
∑
=1
Where,
Yi = actual glucose value at ith observation
Yi′= predicted glucose value at ith observation n = number of the observations
Equation 2 shows APE in percentage. APE will consider as PAPE in equation 2.
Average prediction error in percentage (PAPE) =
100%
n
| − ′|
∑ =1
…………... (2)
According to “± 20% rule”, data or point is accepted if the prediction error is less than or equal
| − ′|
to ± 20%:
100% ≤ 20%
For a better regression model, PAPE should me small and acceptable data should be
more. In order to compare between regression models, these values need to be considered. A
Clarke Error grid analysis graph is used to exhibit the “± 20%” rule. Value within Region A are
within 20% values, Region B contains points that are outside of 20% but not lead to unsuitable
glucose monitoring value, Region C leads to unnecessary treatment, Region D indicates
dangerous failure to detect hypoglycemia or hyperglycemia, and Region E gives the ambiguous
38
data for the treatment of hypoglycemia for hyperglycemia. This graph is usually used to exhibit
the performance of the blood glucose monitor. This is shown in Figure 31 [51].
Figure 31. Clarke Error Grid [51]
2.6.2 Multiple Regression Analysis
In statistics, regression is used to describe a group of approaches that recapitulate the
degree of association between the responses and references. Any regression have to begin with a
proper aspect at the data. Like form where the data is coming, the way it is measured, the
measurement is proper or with noise, number of available observations, units of the data,
magnitudes and ranges of the values of data, and the most important is the way response is
depending the reference. In simple linear regression, a desired variable is predicted from one
response variable. In multiple regression, the desired value is predicted by two or more variables.
Common statistical method used to do this is least-squares regression.
39
CHAPTER 3
PROPOSED NONINVASIVE BLOOD GLUCOSE LEVEL MONITORING
SYSTEM
In this chapter, the proposed method and its working principle to monitor the blood
glucose level in noninvasive way are explained with a block diagram.
3.1
Overview
As per the advantages of multi-sensor approach discussed earlier, the following approach
of using infrared sensor and ultrasonic MEMS for noninvasive blood glucose level monitoring is
proposed. It is calibrated using multiple regression model. It is based on the principle of both
near infrared spectroscopy and photo acoustic spectroscopy.
As both IR sensor and ultrasonic sensor are considered, it is very important to see the
behavior of these sensors to human skin. As the properties of skin and glucose were discussed in
chapter 2, the respective parameters are chosen for the proposed techniques. According to the
absorption spectrum of glucose in blood, it was observed that there are two peaks of the
spectrum: one at 1440 nm and other at 2270 nm. Presence of hemoglobin will not affect the
absorption of IR for glucose or water [52]. At 1440 nm, the absorption of water is also high so
2270 nm is preferable to predict the glucose concentration. For ultrasonic sensor, the density of
medium is considered to measure the concentration of glucose. For 70mg/dl of glucose, the
density will be approximately 1060 kg/m3. As the concentration of glucose will increase, the
density will also increase. Using these values the glucose concentration is calculated in the
proposed method.
40
In this chapter we will discuss about the proposed method in detail using the block
diagram and workflow of the proposed method.
3.2
Proposed Method
IR sensors and Ultrasonic MEMS sensors are used for monitoring the glucose
concentration in a noninvasive approach. Two IR sensor as transmitter and receiver and two
Ultrasonic MEMS as transmitter and receiver are considered here. Proposed method will work
on the principle of NIR spectroscopy and Photo acoustic Spectroscopy. The output from two
receivers will feed to a calibration model to predict the blood glucose concentration. Calibration
model is built using multiple linear regression analysis.
For the measurement of blood glucose concentration in noninvasive approach, it is very
important to choose the proper target location (such as finger webbing, earlobe, tongue, finger,
nasal septum, cheek and lips) for the device on human body. In this work, earlobe is considered
due to some advantages i.e., earlobe has a large supply of blood as it doesn’t contain cartilage
and earlobe makes it easy for NIR and ultrasonic to reach dermis region due to its low thickness.
So two transmitters are placed on one side of the ear lobe and two receivers are placed on other
side of the ear lobe. Block diagram of the proposed technique is explained in next section.
3.3
Block Diagram of the Proposed Method
IR (infrared) and ultrasonic transmitters and IR and ultrasonic receivers are used for the
noninvasive monitoring of blood glucose. The proposed system with IR and ultrasonic MEMS
sensors is illustrated in Figure 32.
41
Figure 32. Block diagram of proposed NIBGM system
IR transmitter incidents light on skin (such as earlobe) in near infrared region of light and
the light returned from the skin has been received by the IR receiver. Similarly, ultrasonic wave
from ultrasonic MEMS sensor generates pressure wave in medium (such earlobe) and is
collected by ultrasonic MEMS receiver. In order to generate ultrasonic wave, piezoelectric
device is used. Results from both sensors are then calibrated to get blood glucose concentration
using multiple linear regression analysis. All the individual parts in that block diagram are
explained below as follows:
Infrared Transmitter: The functional wavelength of 2270 nm will be utilized here for the
experimental purposes. This unit is connected to one side of earlobe.
Ultrasonic Transmitter: A piezoelectric based ultrasound transmitter with 2 MHz of operating
frequency will be supplied for the generation of acoustic wave. This unit is also connected to the
same side of earlobe as Infrared Transmitter.
Infrared Receiver: The blood glucose tends to have a precise vibrational pattern for its respective
concentration measurements tend to change in absorption will be responded by this very
sensitive IR receiver unit.
Ultrasonic Receiver: Again, the piezoelectric based ultrasound receiver with 2 MHz as operating
frequency has been utilized here. The prime task of this unit is to plaid the pattern and
orientations of the generated acoustic waves.
42
Calibration Model: The output values form two receivers will be fed to this model. Using
multiple linear regression analysis, the values are processed in MATLAB to predict the blood
glucose level concentration.
3.4
Working Principle of the Proposed Method
Ultrasonic wave is a sound wave transmitted at a frequency greater than 20 KHz or
beyond the normal hearing range of human. When ultrasonic wave will pass through the
biological tissue, it produces vibrational patterns into the medium. These wave can be generated
from a piezoelectric transducer. Standing waves has maximum and minimum value namely:
antinodal and nodal which are double over a length of a single wavelength. This leads to the
generation of acoustic energy due to its existence in the ultrasonic field. When diameters of
molecule are smaller as compared to the ultrasonic wavelength, radiation force (F) applied over
the volume of molecule (V) having path length of (z) from the pressure node can be represented
from the gradient of the acoustic potential energy of the molecule and is shown in equation 3.
F = − [πP02 Vβw / (2λ)]. Ф (β, ρ).sin 4πz/ λ …………………………….…….. (3)
Where,
Po = amplitude of the ultrasonic waves
λ = wavelength of ultrasonic standing waves.
ρ= density of the material
When the concepts of compressibility factor come into picture, then following equation 4 comes
into picture:
Ф (β, ρ) = [− (βc/ βw)] ………………………………………………………...... (4)
43
Where,
βw = factor of compressibility.
βc = compressibility of molecule present in medium.
ρc = densities of molecules present in medium.
ρw = densities of suspending medium present.
In the air domain, the wave equation which is describe in equation 5 describes the
pressure distribution:
. (- 1/ ρ0 (ΔP)) – ω2P/ ρ0C2 = 0 ……………………………..………....………. (5)
Where,
ρ0 = density of the medium.
P = pressure generated in the medium from ultrasonic radiation.
ω = angular velocity of the radiation.
C = speed of the radiation.
So increase in the glucose concentration in blood will increase the pressure generated
form the transducer. Hence we will be able to monitor the glucose level using the pressure
measurements.
When light from infrared sensor will be applied to the medium, then absorption depends
on the number of molecules present. This is better can be explained by Beer-Lamberts law as
shown in equation 6.
44
A (v) = -log I (v)/ Io (v)…………………………………….………………..…. (6)
Where,
A (v) = absorbance at wavelength of 1/v.
v= wave number.
I= light intensity of the adjacent medium.
I0= light intensity after penetrating through the medium.
Absorbance can also defined in terms of absorptivity coefficient, concentration of
molecule and path length as shown in equation 7.
A (v) = a*b*c ………………………………………………………………….. (7)
Where,
a = absorptivity coefficient
b = path length
c = concentration.
Here, c is considered as the concentration of blood glucose. So, increase in level of
glucose will increase the absorbance. In this way finding the value of glucose level is achieved
by comparing absorbance value to a reference absorbance value.
After getting the value of pressure generated from ultrasonic MEMS and absorbance
from infrared sensor, the system calibration is done using multiple regression model and hence
the prediction of the glucose value is done more accurately.
45
CHAPTER 4
EXPERIMENTAL DETAILS
Proposed multisensor noninvasive blood glucose monitoring system is simulated using
COMSOL and Matlab to evaluate its effectiveness. For ultrasonic sensor, COMSOL
Multiphysics 4.3 software is used in order to measure the concentration of blood glucose level.
For calibration of the system, Matlab multiple regression analysis is used.
4.1
COMSOL Multiphysics 4.3
COMSOL Multiphysics is a powerful cooperative environment for modeling and
analyzing scientific and other problems. It provides an influential integrated desktop location
with a Model Builder, where it is able to simulate complex problems by accessing
functionalities.
In case of simulation, the blood is considered as a medium. Piezoelectric material is used
as ultrasonic wave transceiver (transmitter and receiver). Figure 33 illustrates the geometry of the
ultrasonic MEMS sensor for blood medium where quarter is represented as medium and
rectangle portion is represented as piezoelectric material.
Figure 33. Geometry of ultrasonic MEMS sensor and blood
46
4.1.1 Boundary Conditions and Assumptions for Simulation
Some important assumptions that are made during the experiment include:
 Blood medium is represented as a quarter with radius of 4 mm. 

 Lead free piezoelectric materials are considered for ultrasonic sensors. Size of piezoelectric
device used is 0.25 mm x 0.225 mm. 

 Alternating current (AC) electric potential of 1.5 V is applied over the upper surface of the
transducer and the bottom part is grounded. Frequency is varied from 1 MHz to 4 MHz. It is
observed that at 2 MHz, the generated pressure is high. Hence the frequency of 2 MHz is
used as the exciting frequency in rest of our experiment. 

 It is found that the piezoelectric device gives high pressure at 2MHz [54]. So, 2 MHz
frequency is used as the frequency for simulation. 

 The bottom surface of the piezo transducer is assigned to a roller boundary condition to
prevent it to travel vertically, along the z-axis. The piezoelectric coefficient is dependent on
size due to the strong orientation of axis. When the ultrasonic sensor reaches axis orientation,
the piezoelectric coefficient increases rapidly [55]. 
4.2
Matlab
Matlab code is used to enhance the accuracy by performing calibration of the system. It takes
pressure and absorbance as inputs and provides the blood glucose value as output.
4.2.1 Assumptions for Calibration System
The outputs of sensors are considered as inputs to the calibration model to fine tune the value of
glucose. Multiple linear regression analysis are used for calibration. In multiple linear regression
models, relation between glucose level and other parameters can be defined using equation 8.
47
Yi = β0 + β1Xi1 +... + βp-1Xip-1 +εi ……………………………………………… (8)
Where, Yi is actual glucose value; β0, β1, βp-1 are regression parameters; Xi, Xip-1 are variables
measured from the sensors; here εi is a constant, and p is a regression parameter. The optimal
regression parameter values can be determined by checking the appropriate calibrated value (Q)
by using equation 9 [53].
Q = ∑ (Yi -β0 - β1Xi1 -... - βp-1Xip-1)2 ………………………...………………… (9)
When the regression parameters are estimated, calibration model using multiple
regression analysis can be used to predict the glucose level.
48
CHAPTER 5
RESULTS AND EVALUATION
In this chapter, we present the impact of piezoelectric materials on pressure followed by
pressure generated in blood and predicted blood glucose concentration. Then we compare the
predicted glucose values with those from NIR spectroscopy. Finally, we perform Clark EGA for
the predicted glucose values.
5.1
Noninvasive Monitoring of Blood Glucose Level
For the measurement of blood glucose measurement in a noninvasive way, IR sensor and
ultrasonic MEMS sensor are considered. The simulation for the ultrasonic MEMS sensor for
different piezoelectric devices are done using COMSOL Multiphysics and the calibration model
is done in Matlab. The data related to blood glucose level for IR sensor and Ultrasonic MEMS
sensor are analyzed.
5.1.1 Simulation of Different Piezoelectric Materials
Lead free piezoelectric materials such as Lithium Niobate (LiNbO3; LIN), Barium
Sodium Niobate (Ba2NaNb5O15; BNN), and Barium Titanate (BaTiO3; BT) are only considered
here. Pressures generated due to the piezoelectric materials for given blood glucose
concentration is shown in Table 4. Here, the normal human blood glucose values (from 70 mg/dl
to 449 mg/dl) and blood density (from 1060.00 kg/m3 to 1053.89 kg/m3) are considered.
49
Table 4. Pressure Generated in Blood by Different Piezoelectric Materials
Blood Glucose
Concentration
Density
of Pressure
Blood Medium Generated
(mg/dl)
70
(kg/m3)
1060.00
LIN (Pa x 10 )
-80521.5638
Pressure
Generated
by
by BT (Pa x
-2
10-2)
BNN (Pa x 10 )
-156450.872
-212450.872
87
1060.17
-80524.9506
-156471.570
-212471.570
155
1060.85
-80541.4010
-156572.102
-212572.102
227
1051.57
-80559.0608
-156680.025
-212680.025
340
1052.70
-80586.6389
-156848.561
-212848.561
390
1053.20
-80598.0088
-156918.044
-212918.044
408
1053.38
-80602.3632
-156944.654
-212944.654
415
1053.45
-80604.2985
-156956.481
-212956.481
449
1053.89
-80613.9748
-157015.615
-213015.615
-2
Pressure
by Generated
Experimental results show that the pressure generated due to BT is significantly higher
than that are generated due to LIN and BNN. Therefore, BT is preferred for modelling the
ultrasonic sensor. The waveform of acoustic pressure generated in blood sample with varying
glucose concentration are observed by simulating using BT. Piezoelectric materials with high
lead (such as Lead Zirconate Titanate with more than 60% lead by mass) are not considered due
to possible health hazard.
50
5.1.2 Analysis of Data for Measuring Concentration of Glucose
As IR sensor and ultrasonic sensor are used, the data for both sensors for measuring the
concentration of glucose should be analyzed. In this section first analysis is performed using
ultrasonic MEMS sensor, then using IR sensor and then it is finally analyzed for the proposed
technique.
5.1.2.2 Using Ultrasonic MEMS Sensor
As the glucose concentration of blood increases, it enhances the change in density of
blood. As a result, acoustic pressure generated in blood due to the piezoelectric material varies.
As shown in Figure 3(a) and 3(b), the acoustic pressure generated by the piezoelectric
device BT in blood having glucose concentration 54.05 mg/dl is -4325.3 Pa and for 180.18
mg/dL, pressure is -4330.9 Pa. It should be noted that the (-) sign is used to indicate the pressure
gradient.
Figure 34 (a). Waveform of Acoustic Pressure Generated in Blood of Concentration
54.05mg/dL
51
Figure 34 (b). Waveform of Acoustic Pressure Generated in Blood of Concentration
180.18mg/dL
Generated pressures and absorption values due to the piezoelectric device BT for
various blood glucose concentrations are is shown in Table 5.
It is observed from the Table 5 that with increase in concentration of blood glucose
level, the density of the blood will also increase. Due to change in density, the pressure
generated by the piezoelectric device also increase. This becomes a baseline for the
measurement of blood glucose concentration using ultrasonic MEMS.
52
Table 5. Pressure Generated by BT in Blood for Various Glucose Concentration
Glucose Concentration(mg/dl)
Density(kg/m3)
Pressure generated by BT(Pa)
54.0546
1104.0546
-4325.3
63.0637
1113.0637
-4325.7
68.46916
1118.46916
-4325.9
72.0728
1122.0728
-4326.1
81.0819
1131.0819
-4326.5
86.48736
1136.48736
-4326.7
90.091
1140.091
-4326.9
99.1001
1149.1001
-4327.3
104.50556
1154.50556
-4327.5
108.1092
1158.1092
-4327.7
117.1183
1167.1183
-4328.1
122.52376
1172.52376
-4328.3
126.1274
1176.1274
-4328.5
135.1365
1185.1365
-4328.9
140.54196
1190.54196
-4329.1
144.1456
1194.1456
-4329.3
153.1547
1203.1547
-4329.7
162.1638
1212.1638
-4330.1
171.1729
1221.1729
-4330.5
180.182
1230.182
-4330.9
53
In the proposed NIBGM system, this pressure is collected by ultrasonic receiver. So,
monitoring of blood glucose level can be done using the output from ultrasonic sensor.
5.1.2.1 Using Infrared Sensor
On the basis of NIR Spectroscopy, it is possible to measure the blood glucose level. For a
certain wavelength, the absorptivity coefficient of glucose is constant. Here a wavelength of
2270 nm is considered, hence the absorptivity coefficient as 2.0*10-5 AU/mM mm and thickness
of the medium as 1 mm. Absorption for various glucose concentration was observed [56] and
shown in Table 6.
It is observed that at a particular wavelength, absorption of the infrared depends on
concentration of glucose. So, by measuring the absorption spectrum for particular wavelength, it
is possible to calculate the concentration of blood glucose level.
Table 6. Absorption of IR for Various Glucose Concentration
Glucose(mg/dl)
Absorption(AU)
54.0546
6.7776
63.0637
7.8902
68.46916
7.6038
72.0728
8.1826
81.0819
9.5008
86.48736
8.5716
90.091
9.4532
99.1001
10.667
54
Table 6. (continued)
104.50556
12.0944
108.1092
13.2992
117.1183
13.92
122.52376
14.538
126.1274
14.0454
135.1365
15.1562
140.54196
16.0012
144.1456
16.2904
153.1547
16.218
162.1638
17.7284
171.1729
18.9216
180.182
20.3608
5.1.2.3 Using Proposed Technique
In the proposed multi-sensor NIBGM system, both IR and ultrasonic sensors are
considered. Output values from IR sensor and ultrasonic sensor are captured in terms of
absorbance and pressure, respectively. By using the output values of both IR sensor and
ultrasonic sensor, calibration is performed using multiple regression analysis. After calibration,
the predicted blood glucose concentration is obtained due to the proposed multi-sensor NIBGM
system. Table. 7 shows the output value of both sensors with change in glucose concentration.
55
Table 7. Absorption and Pressure Generated Value for Various Glucose Concentration
Glucose (mg/dl)
Absorption (AU)
Pressure Generated by BT (Pa)
54.0546
6.7776
-4325.3
63.0637
7.8902
-4325.7
68.4692
7.6038
-4325.9
72.0728
8.1826
-4326.1
81.0819
9.5008
-4326.5
86.4874
8.5716
-4326.7
90.0910
9.4532
-4326.9
99.1001
10.6670
-4327.3
104.5056
12.0944
-4327.5
108.1092
13.2992
-4327.7
117.1183
13.9200
-4328.1
122.5238
14.5380
-4328.3
126.1274
14.0454
-4328.5
135.1365
15.1562
-4328.9
140.5420
16.0012
-4329.1
144.1456
16.2904
-4329.3
153.1547
16.2180
-4329.7
162.1638
17.7284
-4330.1
171.1729
18.9216
-4330.5
180.1820
20.3608
-4330.9
56
The output values of the sensor are observed to be changing with change in concentration
of glucose in blood. These values will be fed to the calibration model to predict the blood
glucose level.
5.2
Calibration Model for Measuring Blood Glucose Concentration
As the values from both receivers are available, the device is modelled to get the actual
glucose value. This can be done using the calibration model. During the practical application, a
larger number is received. Hence, the regression analysis method is adapted to build the
calibration model. Calibration model gives the value of glucose by sensing the values from two
receivers. In this section, the glucose value for only IR sensor is predicted by implementing only
NIR Spectroscopy and for proposed technique using regression analysis.
5.2.1 Calibration Model in NIR Spectroscopy using IR Sensor
Using regression analysis and output from IR sensor, the glucose level can be predicted
as shown in Table 8.
From Table 8, we can get the following regression statistics:







Coefficient of multiple correlation (Multiple R) is 0.989239681. 
Coefficient of determination (R Square) is 0.978595146. 
Adjusted R Square is 0.977405987. 
Standard Error is 5.620920783. 
57
Table 8. Predicted Glucose Value for NIR Spectroscopy
Observation
Actual glucose Value(mg/dl)
Predicted Glucose Value for Infrared
Spectroscopy(mg/dl)
1
54.0546
58.98367323
2
63.0637
69.03925908
3
68.46916
66.45079981
4
72.0728
71.68194586
5
81.0819
83.59572734
6
86.48736
75.19769536
7
90.091
83.16552252
8
99.1001
94.13574552
9
104.5056
107.0364675
10
108.1092
117.9253492
11
117.1183
123.5360878
12
122.5238
129.1215202
13
126.1274
124.6694425
14
135.1365
134.7087601
15
140.542
142.3457995
16
144.1456
144.959565
17
153.1547
144.3052198
18
162.1638
157.9560888
19
171.1729
168.7401308
20
180.182
181.7475001
58
5.2.2 Calibration Model in Proposed Technique
For the calibration model of proposed technique, the values from two sensors are
considered. By applying the multiple linear regression analysis, the glucose level can be
predicted as shown in Table 9.
Table 9. Predicted Glucose Value for proposed technique
Observation
Actual glucose Value
Predicted Glucose Value using
Proposed technique
1
54.0546
54.38117445
2
63.0637
63.37451656
3
68.46916
67.87318322
4
72.0728
72.36980099
5
81.0819
81.36265622
6
86.48736
85.8628451
7
90.091
90.35874581
8
99.1001
99.35184827
9
104.5056
103.8464565
10
108.1092
108.3415918
11
117.1183
117.3360985
12
122.5238
121.8326235
13
126.1274
126.3317785
14
135.1365
135.3251248
15
140.542
139.8211122
16
144.1456
144.3184158
17
153.1547
153.3145641
18
162.1638
162.3069642
19
171.1729
171.3001154
20
180.182
180.2926841
59
The following regression statistics is obtained:







Multiple R is 0.999944192. 
R Square is 0.999888388. 
Adjusted R Square is 0.999875257. 
Standard Error is 0.417655706. 
It is observed from Table 8 and Table 9 that the statistical data are in favor of the
proposed technique. In the proposed technique, minimum error is obtained when compared to
NIR spectroscopy and the values of Multiple R and R Square are more when compared to NIR
Spectroscopy.
5.3
Analysis of Error for the Prediction of Glucose Concentration
Although there are lots of advantaged of NIBGM over invasive methods, but still it is
facing some disadvantages like accuracy, sensitivity, specificity, etc. In this section we will
observe the accuracy for NIR Spectroscopy and Proposed technique by calculating the error
related to predicted glucose value.
5.3.1 Error Analysis for Glucometer using NIR Spectroscopy and Proposed Technique
In the proposed multi-sensor NIBGM system, both IR and ultrasonic sensors are
considered. Output values from IR sensor and ultrasonic sensor are captured in terms of
absorbance and pressure, respectively. By using the output values of both IR sensor and
ultrasonic sensor, calibration is performed using multiple regression analysis. After calibration,
the predicted blood glucose concentration are obtained due to the proposed multi sensor NIBGM
system.
60
In this section the accuracy of NIR Spectroscopy and Proposed technique are analyzed
and as shown in Table 10.
Table 10. Predicted Glucose Value for IR spectroscopy and Proposed NIBGM system
Number of
Experiments
Actual
Glucose
Value
(mg/dl)
Predicted
Glucose
Value for
only IR
Spectroscopy
(mg/dl) [22]
Residuals for
Infrared
Spectroscopy
1
54.0546
58.98367
-4.92907
Predicted
Glucose
Value Using
Proposed
NIBGM
System
(mg/dl)
54.3811745
2
63.0637
69.03926
-5.97556
63.3745166
-0.31080
3
68.4692
66.45080
2.01836
67.8731832
0.59598
4
72.0728
71.68195
0.39085
72.3698010
-0.29700
5
81.0819
83.59573
-2.51383
81.3626562
-0.28080
6
86.4874
75.19770
11.28966
85.8628451
0.62451
7
90.0910
83.16552
6.92548
90.3587458
-0.26770
8
99.1001
94.13575
4.96435
99.3518483
-0.25170
9
104.5060
107.03650
-2.53091
103.8464570
0.65910
10
108.1090
117.92530
-9.81615
108.3415920
-0.23240
11
117.1180
123.53610
-6.41779
117.3360990
-0.21780
12
122.5240
129.12150
-6.59776
121.8326240
0.69114
13
126.1270
124.66940
1.45796
126.3317790
-0.20440
14
135.1370
134.70880
0.42774
135.3251250
-0.18860
15
140.5420
142.34580
-1.80384
139.8211120
0.72085
16
144.1460
144.95960
-0.81396
144.3184160
-0.17280
17
153.1550
144.30520
8.8495
153.3145640
-0.15990
18
162.1640
157.95610
4.20771
162.3069640
-0.14320
19
171.1730
168.74010
2.43277
171.3001150
-0.12720
20
180.1820
181.74750
-1.56550
180.2926840
-0.11070
61
Residuals for
Proposed
Technique
-0.32660
It is observed that the proposed NIBGM technique provides better accuracy (as residuals
are lower) when compared with that due to IR spectroscopy. Analysis of error is shown in
Figure 35 by actual and predicted glucose level.
Predicted Glucose Value(mg/dL)
174
159
144
129
114
99
84
69
54
54
69
84
99
114
129
144
159
174
Actual Glucose Concentration (mg/dL)
NIR Spectroscopy
Proposed Technique
Actual value
Figure 35. Predicted Glucose Value vs Actual Glucose Value for IR spectroscopy and Proposed
NIBGM system.
It is observed from Figure 35 that the predicted value of glucose for NIR Spectroscopy is
not linear to actual value of glucose but the predicted value of glucose for proposed technique is
linear to actual value of glucose and hence leads to more accurate results.
5.3.2 Clarke Error Grid Analysis for the Proposed Technique
Finally, Clarke error grid analysis is performed to validate the proposed NIBGM system.
The Clarke EGA is to quantify clinical accuracy of patient estimates of their current blood
glucose as compared to the blood glucose value obtained in their meter. It can also be used to
62
quantify the clinical accuracy of blood glucose estimates generated by meters as compared to a
reference value.
As illustrated in Figure 36, predicted glucose concentration due to the proposed NIBGM
system is plotted for Clarke error grid analysis.
Figure 36. Clarke error grid analysis for the proposed NIBGM system
As shown in the Figure 36, the predicted glucose values due to the proposed system are
in Region A which shows improved accuracy.
5.3.3 Important Hardware Considerations
While making decision to integrate infrared sensors and ultrasonic sensors into a single
system, following are some important issues need to be considered.
63
 Components Required: Components required to integrate both sensors into single system
varies from applications to applications. For most applications, to connect sensors, one will
need a microcontroller to control the output of the integrated system. 

 Power Supply: Power supply should be chosen based on the application with a maximum of 5
V is required for microcontroller. It is extremely important not to exceed the power limit of
sensors. 

 Compatibility: There are components that may not be compatible with a specific
microcontroller. To keep the system low-cost and simple, one should address the
compatibility first. 
Mainly biocompatible hardware are preferred for measuring blood glucose level. Two
infrared sensor, two ultrasonic sensors and microcontroller are used for the proposed technique.
Overall estimated cost of proposed technique is shown in Table 11.
Table 11. Estimated overall cost of the device based on proposed technique
Components Required
Estimated Price ($)
Infrared sensor (2)
50
Ultrasonic MEMS (2)
150
Microcontroller (1)
175
Total Estimated Cost
375
64
Blood glucose monitoring based on multi-sensor approach which is now available in market is
GlucoTrack which costs of $2000 with ear clip of $100 which needs to be replaced every six
months. After comparing the cost of the available device in market and with estimated cost of
proposed technique, cost of the device based on proposed technique is less.
65
CHAPTER 6
CONCLUSIONS AND FUTURE EXTENSIONS
We hope the experimental results and discussion presented in this work motivates the
interested scholars into considering research in the challenging but prosperous area of
noninvasive blood glucose monitoring. Existing NIBGM systems can be upgraded with the
proposed design for accuracy improvement at low cost. This chapter concludes the work and
offers some possible future extensions.
6.1
Conclusions
Both long term and short term complications from diabetes can be reduced through
suggest diet, physical exercise, and proper medication. In order to determine the appropriate drugs,
blood glucose concentration is regularly monitored. However, traditional invasive blood glucose
monitoring systems are expensive, inconvenience, and painful (due to pricking fingers). In this work,
a promising multisensory noninvasive blood glucose monitoring system is proposed.
The proposed multi-sensor approach is based on the principle of infrared spectroscopy
and photo acoustic spectroscopy. In addition to the IR sensors, ultrasonic MEMS technology is
used. A simulation model is developed using COMSOL Multiphysics software. Calibration is
done using Matlab multiple regression analysis method to improve accuracy. Behavior of
ultrasonic MEMS is studied for different blood glucose concentration. The simulation results are
compared with results from previous studies and cross validated with actual glucose
concentration to justify the proposed NIBGM system. The Clarke EGA is completed to quantify
the accuracy of the proposed system.
66
Experimental results show that the proposed NIBGM system generates lower residuals
when compared with those from a recently introduced noninvasive IR sensor-based approach.
The Clarke EGA suggests that the proposed NIBGM device should be medically acceptable.
Thus, the proposed NIBGM device will help diabetic patients monitor blood glucose level better
because this device is painless and easy to use.
6.2
Future Extensions
The proposed method can be extended to offer the following features:

Wi-Fi or similar technology as an extension to the proposed multi-sensor NIBGM system so
that the blood glucose values can be stored in a remote machine for further analysis. 


Monte Carlo simulation for the calibration model of the proposed technique to attend better
accuracy. 
67
REFERENCES
68
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